How would you create and display an accuracy metric in keras for a regression problem, for example after you round the predictions to the nearest integer class?
While accuracy is not itself effectively defined conventionally for a regression problem, to determine ordinal classes/labels for data, it is suitable to treat the problem as a regression. But then it would be convenient to also calculate an accuracy metric, whether it be kappa or something else like that. Here is a basic keras boilerplate code to modify.
from keras.models import Sequential from keras.layers.core import Dense, Activation model = Sequential() model.add(Dense(10, 64)) model.add(Activation('tanh')) model.add(Dense(64, 1)) model.compile(loss='mean_absolute_error', optimizer='rmsprop') model.fit(X_train, y_train, nb_epoch=20, batch_size=16) score = model.evaluate(X_test, y_test, batch_size=16)
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Answer
I use rounded accuracy like this:
from keras import backend as K def soft_acc(y_true, y_pred): return K.mean(K.equal(K.round(y_true), K.round(y_pred))) model.compile(..., metrics=[soft_acc])